Leaders use data to leave laggards in their wake

A recent study by Boston Consulting Group (BCG) found that “data-led” organisations can expect to see 20 to 30 per cent EBITDA gains over companies that have not embraced data-driven decision making . Choosing to leverage data can also deliver improved staff and customer satisfaction, increased innovation and reduced operating risk.

Despite these clear advantages, the number of organisations making the move to a data-driven culture resemble a steady trickle rather than a raging river. This is, at least in part, because the challenges in making a transition to a data-led way of doing business are not insignificant.

Cultural impediments are the issue for some, while a lack of technology budget or know-how are the primary blockers for other businesses. Surmounting the hurdle of fragmented and disorganised data is the issue for others.

Play the long game

The cultural impediments to data-centricity are rarely a lack of will, or the presence of active “blockers” within the organisation. Instead, they tend to revolve around an absence of proactive intent, and the failure to realise that shifting organisational mindset in a fundamental way is a large task that takes considerable sweat equity.

To embed data in organisational processes requires buy-in from employees at all levels of the organisation. Not just conceptual buy in though — it requires staff to both understand and accept new data-driven processes, and choose to change their actions on a day-by-day basis. This takes time.

Building a roadmap that acknowledges there is a learning curve for the organisation and sets a realistic timeframe to adopt various data-driven practices is critical to ensure that progress is not halted at the first sign of resistance or misunderstanding.

Invest in talent early enough to matter

The other cultural hurdle that organisations hit when moving towards data-centricity is a talent gap at the time they need it most. To execute against a data-driven strategy requires two specific types of talent: data strategists and data scientists. Good practitioners in both of these fields are in high demand and hard to source. More importantly, they need a period of time to learn the organisation’s commercial and data environments before they can operate at maximum impact. For a strategist, this learning curve is measured in weeks. For a data scientist, operating at the complex technical end of the business, it is likely to be several months before they are delivering at peak capacity. Given scarcity and learning curves, then, it is necessary to recruit for these specialists far before you think you need them. Failing to do so can result in stalled momentum as critical projects are put on hold “pending resources”.

Centralise data

In some cases, the barrier to becoming data-driven is a simple but significant technology hurdle: the capture and storage of data. A centralised “source of truth” repository is a minimum prerequisite for data-driven decision making, but the process of corralling and cataloguing data can seem an overwhelming task. This is particularly true when an organisation is geographically diverse or has grown organically over time.

The reality is that establishing and populating a robust data infrastructure is a gruelling task. It is a time-consuming, intricate and at times frustrating process — but those who do push through and establish a centralised data asset see gains in value and opportunity that more than justify the effort involved.

Don’t relegate data to IT

The quickest way to ensure data doesn’t infuse decision making is to assign its management and dissemination to “the IT department”. While IT as a business discipline is a tremendously valuable part of the modern enterprise, its remit in regards to data is infrastructure and security — not operational use. Placing the lifeblood of decision making in the hands of a department designed to parcel out access in the most secure manner possible means data architecture is likely to be optimised for storage and security, not for everyday use. Data should ideally have its own centre of excellence, but if it must sit within an existing business unit then it should live within marketing or sales — a business unit that is outward-focused and oriented toward insight rather than structure.

Although there are substantial challenges in moving towards a data-led world view, they are by no means insurmountable. The characteristics that define a healthy organisation — hiring well, open communication, a well-thought-out road map — eliminate many of the cultural challenges to data-centricity. Creating a centralised data asset is a big challenge, but the increased prevalence of good ETL and data-management tools has reduced the manual effort in this area. And as for data ownership, most marketeers are more than willing to put up their hands to own and manage data assets, given the exciting opportunities in data-driven marketing and personalised offers.

Becoming and remaining truly data-led does take serious work — but the payoff in bottom-line returns and innovation opportunities makes it a task well worth pursuing.

About the Which-50 Digital Intelligence Unit

This article was produced for ADMA by Which-50. ADMA is a member of the Which-50 Digital Intelligence Unit and this story appears in The Future of Marketing.

The Author

Anna Russell

Anna Russell is a data scientist and founder of Polynomial, a consultancy that helps forward-thinking brands find meaning and value in data. Before starting Polynomial, Anna spent a decade in research, analytics and management consulting, working with clients in the financial services, technology and media sectors. A self-confessed geek, with degrees in economics, psychology and urban planning, Anna is fascinated by what makes people and places tick.A self-confessed geek, with degrees in economics, psychology and urban planning, Anna is fascinated by what makes people and places tick.

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